Associations Between Distinct Co-occurring Substance Use Disorders and Receipt of Medications for Opioid Use Disorder in the Veterans Health Administration
Bibliographic record
Abstract
OBJECTIVES: Among people with opioid use disorder (OUD), having a co-occurring substance use disorder (SUD) is associated with lower likelihood of receiving OUD treatment medications (MOUD). However, it is unclear how distinct co-occurring SUDs are associated with MOUD receipt. This study examined associations of distinct co-occurring SUDs with initiation and continuation of MOUD among patients with OUD in the national Veterans Health Administration (VA). METHODS: Electronic health record data were extracted for outpatients with OUD who received care August 1, 2016, to July 31, 2017. Analyses were conducted separately among patients without and with prior-year MOUD receipt to examine initiation and continuation, respectively. SUDs were measured using diagnostic codes; MOUD receipt was measured using prescription fills/clinic visits. Adjusted regression models estimated likelihood of following-year MOUD receipt for patients with each co-occurring SUD relative to those without. RESULTS: Among 23,990 patients without prior-year MOUD receipt, 12% initiated in the following year. Alcohol use disorder (adjusted incidence rate ratio [aIRR], 0.80; 95% confidence interval [CI], 0.72-0.90) and cannabis use disorder (aIRR, 0.78; 95% CI, 0.70-0.87) were negatively associated with initiation. Among 11,854 patients with prior-year MOUD receipt, 83% continued in the following year. Alcohol use disorder (aIRR, 0.94; 95% CI, 0.91-0.97), amphetamine/other stimulant use disorder (aIRR, 0.94; 95% CI, 0.90-0.99), and cannabis use disorder (aIRR, 0.95; 95% CI, 0.93-0.98) were negatively associated with continuation. CONCLUSIONS: In this study of national VA outpatients with OUD, those with certain co-occurring SUDs were less likely to initiate or continue MOUD. Further research is needed to identify barriers related to specific co-occurring SUDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".